An increasing array of tools is being developed using artificial intelligence (AI) and machine
learning (ML) for cancer imaging. The development of an optimal tool requires …

The high-throughput extraction of quantitative imaging features from medical images for the
purpose of radiomic analysis, ie, radiomics in a broad sense, is a rapidly developing and …

Breast cancer is one of the most frequent malignancies among women worldwide. Based on
clinical and molecular features of breast tumors, patients are treated with chemotherapy …

Medical imaging techniques, such as mammography, ultrasound and magnetic resonance
imaging, plays an integral role in the detection and characterization of breast cancer …

Background: Ultrasound (US) is a fundamental diagnostic tool in breast imaging. However,
US remains an operator-dependent examination. Research into and the application of …

In this study, a novel deep learning-based methodology was investigated to predict breast
cancer response to neo-adjuvant chemotherapy (NAC) using the quantitative ultrasound …

Background The biological phenotype of tumours evolves during neoadjuvant
chemotherapy (NAC). Accurate prediction of pathological complete response (pCR) to NAC …

Purpose The current study aimed to evaluate the association of endorectal ultrasound (EUS)
radiomics features at different denoising filters based on machine learning algorithms and to …

Breast cancer is the most common cancer in women worldwide. Providing accurate and
efficient diagnosis, risk stratification and timely adjustment of treatment strategies are …

Objectives: To explore the predictive value of radiomics nomogram using pretreatment
ultrasound for disease-free survival (DFS) after resection of triple negative breast cancer …